arXiv:2607. 20214v1 Announce Type: cross Abstract: The quadratic $N\times N$ attention score matrix remains a central obstacle to extending Transformers to longer input lengths.
The paper introduces Outlier-Aware Attention Linearization (OAL), a method that improves linearized softmax attention by explicitly accounting for a few outlier dimensions that disproportionately affect query-key dot products. OAL builds on a second-order Taylor approximation, uses a Hadamard-product formulation to expose dimension-specific contributions, and groups dimensions with similar scales to share coefficients while treating outliers separately. Experiments on Qwen2.5-0.5B show an 8.9% reduction in frozen-attention output MSE, and fine-tuning with LoRA lowers WikiText-103 perplexity from 31.71 to 27.50 while boosting performance on PIQA, HellaSwag, and ARC-E.
arXiv:2607. 00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning.
arXiv:2509. 07963v2 Announce Type: replace Abstract: The core component of attention is the scoring function, which transforms the inputs into low-dimensional queries and keys and takes the dot product of each pair.
arXiv:2609.37261v1 Announce Type: new Abstract: Softmax attention is ubiquitous in modern machine learning, but its quadratic scaling with sequence length makes it costly. To reduce this cost, attent...
arXiv:2608. 09558v1 Announce Type: new Abstract: How expressive is prompting a transformer?
arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.
The paper argues that sparse attention should be viewed as a matrix approximation problem rather than merely selecting large scalar entries from the attention matrix. It introduces Matrix Approximation Sparse Attention (MASA), a method that replaces raw attention-mass ranking with a closed‑form score reflecting the reduction in matrix‑product approximation error. MASA can be integrated into existing sparse attention frameworks and, according to extensive experiments, consistently improves accuracy across various methods, benchmarks, and LLM backbones.
arXiv:2607. 23050v1 Announce Type: new Abstract: Neural scaling laws describe how loss decreases as models, data, and compute grow, but they do not answer a prior question: for a fixed task, what is the minimum model capacity required to solve it?
Large Language Models (LLMs) achieve strong performance across many domains, but their efficiency is limited by the quadratic cost of attention with respect to prompt length. Sparse attention reduces...
arXiv:2603.02561v2 Announce Type: replace-cross Abstract: Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic co...
arXiv:2606. 27748v1 Announce Type: cross Abstract: Transformer models rely on attention mechanism to capture long-range dependencies but suffer from quadratic complexity, limiting their scalability to long sequences.